Fuzzy theory is being widely applied for the purpose of utilizing knowledge, which is difficult to describe by mathematical expressions of know-how possessed by skilled engineers and technicians, in automatic control, decision making and automatic control of controlled systems having strong non-linearity, such as in fault diagnosis.
Knowledge for fuzzy reasoning in many cases is expressed by a plurality (one set) of rules described in an "If, then" format. An antecedent generally contains a plurality of fuzzy propositions (pairs of input variables and membership functions) connected by "and". A consequent also contains a plurality of fuzzy propositions (pairs of output variables and membership functions).
A set of fuzzy reasoning rules of this kind is stored in a general-purpose digital computer programmed to make fuzzy reasoning operations possible, or in a fuzzy processor having an architecture dedicated to fuzzy reasoning operations. The digital computer or fuzzy processor executes fuzzy reasoning operations by applying given input data to the rules. A fuzzy reasoning operation often is executed by so-called serial processing, in which a plurality of established rules are executed sequentially one at a time.
There are occasions where a set of fuzzy reasoning rules contains identical fuzzy propositions. The reason for this is that redundancy in parts of the rules is permitted in rule creation. In a case where there the number of rules is very large, redundant fuzzy propositions also increase of their own accord and on occasion become too prolix.
Since fuzzy reasoning operations often are executed by serial processing, as set forth above, the same operation is repeated a plurality of times if some of the rules are redundant. The result is a decline in processing efficiency and a reduction in computing speed.
An object of the present invention is to make it possible to obtain a high processing efficiency even if parts of the rules in a set of rules are redundant.